International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025
p-ISSN: 2395-0072
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SymptoDx: An AI Based Diseases Prediction Using Symptoms and Hospital Recommendation Model PROF. S. P. KULLARKAR1, YASH KAMBALE2, HARSHAL DANGARE3, ROHIT PATLE4, RITESH KALAMBE5 1,2,3,4,5,Artificial Intelligence and Data Science K.D.K. College of Engineering Nagpur, India
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Abstract - Healthcare accessibility and early diagnosis
diseases. Unlike conventional systems, SymptoDx AI does not stop at prediction—it goes a step further by recommending nearby hospitals with the relevant specialties, using the integration of the Google Maps API. This ensures that patients are not only made aware of possible health conditions but are also guided toward appropriate medical care promptly.
remain major challenges in modern medicine. Patients often experience delays in receiving appropriate medical care due to the lack of reliable self-assessment tools. Traditional symptom checkers are usually rule-based, limited in scope, and incapable of guiding patients to the right healthcare facilities. To overcome these challenges, SymptoDx AI is developed as an intelligent, AI-driven healthcare support system. The proposed system utilizes machine learning models, specifically ensemble techniques such as Random Forest and XGBoost, to analyze patient-reported symptoms and predict possible diseases with improved accuracy.
A key strength of SymptoDx AI is its explainability feature. By leveraging interpretability techniques such as SHAP or LIME, the system provides a clear explanation of how a prediction was made, highlighting the relationship between symptoms and possible diseases. This transparency enhances trust and ensures that patients and healthcare providers can use the tool confidently. Additionally, the user-friendly interface is designed to be accessible to patients from diverse backgrounds, making the system practical for both urban and rural healthcare settings.
A key feature of SymptoDx AI is the integration of the Google Maps API, which provides real-time hospital recommendations based on the predicted condition and the patient’s location, ensuring timely access to specialized care. Furthermore, the system incorporates an explainable AI layer using techniques like SHAP or LIME to provide transparency in predictions, thereby increasing patient trust and usability. The system is designed with a user-friendly interface to make healthcare insights accessible to patients, hospitals, and telemedicine platforms.
Overall, SymptoDx AI offers a comprehensive, patientcentered approach to digital healthcare. By integrating disease prediction, hospital recommendation, and explainability, the project aims to reduce diagnostic delays, enhance patient awareness, and strengthen the bridge between individuals and healthcare providers. This project not only demonstrates the potential of AI in healthcare but also highlights the importance of making such technologies reliable, transparent, and accessible for widespread adoption.
Key Words: Symptom Analysis, Disease Prediction, Machine Learning, Explainable AI, Healthcare Technology, Hospital Recommendation System
I. INTRODUCTION Healthcare is one of the most critical sectors where timely access to accurate information can significantly impact patient outcomes. In many cases, patients delay seeking medical attention because they are uncertain about the severity of their symptoms or do not know which healthcare facility to approach. Traditional symptom checkers available online are often rule-based, limited in scope, and fail to reliable or personalized guidance. This gap creates the need for a more intelligent, user-friendly, and transparent solution that can empower patients to make informed healthcare decisions.
The system is designed with a user-friendly interface to ensure accessibility for people from diverse backgrounds, including those with minimal technical expertise. Its applicability extends beyond individual patients—it can serve as a digital triage tool in hospitals, support telemedicine platforms, and even assist public health authorities in monitoring symptom trends for early detection of outbreaks.
II. LITERATURE SURVEY Recent advancements in artificial intelligence (AI) have shown significant potential in improving healthcare navigation, disease prediction, and patient support systems. However, a review of existing studies reveals certain limitations that highlight the need for more comprehensive solutions such as SymptoDx AI.[1]
SymptoDx AI is designed as an innovative system that addresses this challenge by combining the power of machine learning, explainable AI, and location-based services. The system allows users to input their symptoms, which are then analyzed by advanced ensemble models such as Random Forest and XGBoost to predict potential
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